The primary focus of autonomous vehicle development has been optimising transportation on public roads recently, improving road safety, reducing traffic congestion, and enhancing overall traffic efficiency. However, the potential applications of autonomous vehicles extend far beyond roadways. Off-road environments, such as agricultural fields, forests, mines, disaster-affected zones, and military operational areas, are becoming increasingly important in developing autonomous technologies. Traditionally, path planning was approached statically with Cross-Country Movement (CCM) maps, where the terrain was classified into GO, SLOW GO, and NO GO zones based on different vehicle types and weather conditions. Decision-making was in the field and relied heavily on human experience. In the case of automation, the approach could be similar. Before the mission, CCM map development is required, considering the vehicle parameters and cross-country movement criteria. Using this prior information, a preliminary route can be generated and uploaded to the vehicle with the map. The route can be refined by utilising on-board sensor data and the CCM map. This study focuses on the land surveying aspects of off-road mobility modelling. Using data from large-scale measurements and point clouds, essential terrain attributes were derived and organised into layers within a Geographic Information System (GIS) environment. These layers were then used to create cost maps, which model the challenges and preferences for vehicle movement across different terrains, considering the detected obstacles. An optimal route was planned based on the generated layers, demonstrating how geospatial data integration and advanced GIS analysis can support autonomous vehicle path planning in complex off-road environments.